Do you want to recognize the most suitable models for analysis of statistical data sets? This book provides a hands-on practical guide to using the most suitable models for analysis of statistical data sets using EViews - an interactive Windows-based computer software program for sophisticated data analysis, regression, and forecasting - to define and test statistical hypotheses. Rich in examples and with an emphasis on how to develop acceptable statistical models, Time Series Data Analysis Using EViews is a perfect complement to theoretical books presenting statistical or econometric models for time series data. The procedures introduced are easily extendible to cross-section data sets. The author: • Provides step-by-step directions on how to apply EViews software to time series data analysis • Offers guidance on how to develop and evaluate alternative empirical models, permitting the most appropriate to be selected without the need for computational formulae • Examines a variety of times series models, including continuous growth, discontinuous growth, seemingly causal, regression, ARCH, and GARCH as well as a general form of nonlinear time series and nonparametric models • Gives over 250 illustrative examples and notes based on the authors own empirical findings, allowing the advantages and limitations of each model to be understood • Describes the theory behind the models in comprehensive appendices • Provides supplementary information and data sets An essential tool for advanced undergraduate and graduate students taking finance or econometrics courses. Statistics, life sciences, and social science students, as well as appliedresearchers, will also find this book an invaluable resource.
Author(s): I. Gusti Ngurah Agung
Series: Statistics in Practice
Edition: New
Publisher: Wiley
Year: 2008
Language: English
Pages: 635
Tags: Финансово-экономические дисциплины;Эконометрика;Анализ экономических данных в Eviews;
TIME SERIES DATA ANALYSIS USING EVIEWS......Page 2
Contents......Page 12
Preface......Page 20
1.2 Basic options in EViews......Page 24
1.3.2 Creating a workfile using EViews 4......Page 26
1.4.1 Basic descriptive statistical summary......Page 30
1.4.3 Descriptive statistics by groups......Page 34
1.4.4 Graphs over times......Page 35
1.4.6 Correlation matrix......Page 38
1.4.7 Autocorrelation and partial autocorrelation......Page 40
1.4.8 Bivariate graphical presentation with regression......Page 41
1.5 Special notes and comments......Page 42
1.6 Statistics as a sample space......Page 45
2.2 Classical growth models......Page 48
2.3.1 First-order autoregressive growth models......Page 52
2.3.2 AR(p) growth models......Page 53
2.4. Residual tests......Page 55
2.4.1 Hypothesis of no serial correlation......Page 56
2.4.3 Hypothesis of the normality assumption......Page 57
2.4.4 Correlogram Q-statistic......Page 58
2.5 Bounded autoregressive growth models......Page 61
2.6 Lagged variables or autoregressive growth models......Page 64
2.6.1 The white estimation method......Page 65
2.6.2 The Newey–West HAC estimation method......Page 66
2.6.4 Mixed lagged-variables autoregressive growth models......Page 67
2.6.5 Serial correlation LM test for LV(2,1)_GM......Page 71
2.7.1 Basic polynomial growth models......Page 72
2.7.2 Special polynomial growth models......Page 78
2.8 Growth models with exogenous variables......Page 79
2.9 A Taylor series approximation model......Page 82
2.10.2 Translog additive growth models......Page 83
2.10.3 Some comments......Page 86
2.10.4 Growth model having interaction factors......Page 87
2.10.5 Trigonometric growth models......Page 92
2.11.1 The classical multivariate growth model......Page 93
2.11.2 Modified multivariate growth models......Page 97
2.11.3 AR(1) multivariate general growth models......Page 101
2.12 Multivariate AR(p) GLM with trend......Page 102
2.12.1 Kernel density and theoretical distribution......Page 111
2.13.1 The simplest multivariate autoregressive model......Page 118
2.13.2 Multivariate autoregressive model with two-way interactions......Page 123
2.13.3 Multivariate autoregressive model with three-way interactions......Page 125
2.14.1 The true population model......Page 127
2.14.2 Near singular matrix......Page 128
2.14.3 ‘To Test or Not’ the assumptions of the error terms......Page 130
2.15.1 The lagged endogenous variables: first autoregressive model with trend......Page 136
2.15.2 The lagged endogenous variables: first autoregressive model with exogenous variables and trend......Page 137
2.15.3 The mixed lagged variables: first autoregressive model with trend......Page 138
2.16 Generalized multivariate models with time-related effects......Page 141
3.2 Piecewise growth models......Page 144
3.2.1 Two-piece classical growth models......Page 145
3.3.1 Two-piece linear growth models......Page 152
3.4.1 Two-piece quadratic growth models......Page 159
3.4.2 Two-piece third-degree bounded growth model......Page 160
3.6 Alternative discontinuous growth models......Page 161
3.7.1 Chow’s breakpoint test......Page 178
3.7.2 Chow’s forecast test......Page 181
3.8 Generalized discontinuous models with trend......Page 182
3.8.1 General two-piece univariate models with trend......Page 183
3.8.2 Special notes and comments......Page 191
3.8.3 General two-piece multivariate models with trend......Page 194
3.9 General two-piece models with time-related effects......Page 197
3.10 Multivariate models by states and time periods......Page 203
3.10.1 Alternative models......Page 205
3.10.2 Not recommended models......Page 206
4.1 Introduction......Page 208
4.2.2 The cell-means models......Page 209
4.2.3 The lagged-variable models......Page 215
4.2.5 Lagged-variable autoregressive models......Page 224
4.3 Bivariate seemingly causal models......Page 226
4.3.1 The simplest seemingly causal models......Page 227
4.3.2 Simplest models in three-dimensional space......Page 234
4.3.3 General univariate LVAR(p,q) seemingly causal model......Page 235
4.4.1 Simple models in three-dimensional space......Page 243
4.4.2 General LVAR(p,q) with exogenous variables......Page 246
4.5 System equations based on trivariate time series......Page 249
4.6 General system of equations......Page 251
4.7.1 Homogeneous time series models......Page 255
4.7.2 Heterogeneous time series models......Page 256
4.8 General discontinuous seemingly causal models......Page 261
4.9 Additional selected seemingly causal models......Page 266
4.9.2 A Three-dimensional bounded semilog linear model......Page 267
4.9.3 Time series Cobb–Douglas models......Page 268
4.9.4 Time series CES models......Page 272
4.10.2 Other unexpected models......Page 279
4.10.3 The principal component factor analysis......Page 280
5.2 Specific cases of growth curve models......Page 282
5.2.1 Basic polynomial model......Page 283
5.2.3 Heteroskedasticity-consistent covariance (White)......Page 285
5.3 Seemingly causal models......Page 287
5.3.1 Autoregressive models......Page 288
5.4.1 The basic lagged-variable model......Page 298
5.4.3 Generalized lagged-variable autoregressive model......Page 305
5.5 Cases based on the US domestic price of copper......Page 313
5.5.1 Graphical representation......Page 314
5.5.2 Seemingly causal model......Page 316
5.5.3 Generalized translog linear model......Page 319
5.5.4 Constant elasticity of substitution models......Page 323
5.5.5 Models for the first difference of an endogenous variable......Page 327
5.5.6 Unexpected findings......Page 329
5.5.7 Multivariate linear seemingly causal models......Page 333
5.6 Return rate models......Page 334
5.7 Cases based on the BASICS workfile......Page 337
5.7.1 Special notes......Page 340
6.1 Introduction......Page 342
6.2 The VAR models......Page 343
6.2.1 The basic VAR model......Page 344
6.2.3 Cases based on the demo_modified workfile......Page 346
6.2.4 The VAR models with dummy variables......Page 364
6.2.5 Selected VAR models based on the US domestic price of copper data......Page 367
6.3.1 The basic VEC model......Page 377
6.3.2 General equation of the basic VEC models......Page 383
6.3.3 The VEC models with exogenous variables......Page 384
6.3.4 Some notes and comments......Page 389
6.4 Special notes and comments......Page 403
7.1 Introduction......Page 404
7.2 Should we apply instrumental models?......Page 406
7.3 Residual analysis in developing instrumental models......Page 411
7.3.1 Testing an hypothesis corresponding to the instrumental models......Page 412
7.3.2 Graphical representation of the residual series......Page 414
7.4 System equation with instrumental variables......Page 415
7.5 Selected cases based on the US_DPOC data......Page 418
7.6 Instrumental models with time-related effects......Page 423
7.7 Instrumental seemingly causal models......Page 424
7.7.1 Special notes and comments......Page 428
7.8.1 Simple multivariate instrumental models......Page 429
7.8.2 Multivariate instrumental models......Page 432
7.9 Further extension of the instrumental models......Page 440
8.2 Options of ARCH models......Page 442
8.3.1 Simple ARCH models......Page 443
8.4.1 ARCH models with one exogenous variable......Page 447
8.4.2 ARCH models with two exogenous variables......Page 448
8.4.3 Advanced ARCH models......Page 452
8.5.1 General GARCH variance series for the GARCH/TARCH model......Page 459
8.5.2 General GARCH variance series for the EGARCH model......Page 460
8.5.3 General GARCH variance series for the PARCH model......Page 461
8.5.4 General GARCH variance series for the component ARCH(1,1) model......Page 462
8.5.5 Special notes on the GARCH variance series......Page 463
9.1 Introduction......Page 464
9.2.1 Simple unit root test......Page 465
9.2.2 Unit root test for higher-order serial correlation......Page 469
9.2.3 Comments on the unit root tests......Page 470
9.3 The omitted variables tests......Page 471
9.4 Redundant variables test (RV-test)......Page 477
9.5 Nonnested test (NN-test)......Page 479
9.6 The Ramsey RESET test......Page 482
9.7 Illustrative examples based on the Demo.wf1......Page 484
10.1 Introduction......Page 492
10.2 Classical growth models......Page 494
10.3 Generalized Cobb–Douglas models......Page 496
10.3.1 Cases based on the Demo.wf1......Page 497
10.3.2 Cases based on the BASIC.wf1......Page 500
10.3.3 Cases based on the US_DPOC data......Page 502
10.4 Generalized CES models......Page 514
10.5 Special notes and comments......Page 516
10.6.1 Cases based on Demo.wf1......Page 517
10.6.2 Cases based on the US_DPOC data......Page 520
11.1 What is the nonparametric data analysis......Page 526
11.2.1 Simple moving average estimates......Page 527
11.2.2 The weighted moving average estimates......Page 529
11.3 Measuring the best fit model......Page 531
11.4.1 The moving average models......Page 532
11.4.2 The autoregressive moving average models......Page 536
11.4.3 The ARMA models with covariates......Page 537
11.5.1 The Hardle moving average models......Page 539
11.5.2 The nearest neighbor fit......Page 540
11.5.3 Mathematical background of the nearest neighbor fit......Page 541
11.6 The local polynomial Kernel fit regression......Page 545
11.7 Nonparametric growth models......Page 547
A.1 The simplest model......Page 550
A.1.2 Properties of the error terms......Page 551
A.1.3 Maximum likelihood estimates......Page 552
A.2.1 Properties of the parameters......Page 553
A.2.2 Autocorrelation function of an AR(1) model......Page 554
A.2.3 Estimates of the parameters......Page 555
A.3.2 Autocorrelation function of an AR(2) model......Page 556
A.3.3 Estimates of the parameters......Page 557
A.4 First-order moving average model......Page 558
A.5 Second-order moving average model......Page 559
A.6 The simplest ARMA model......Page 560
A.7.1 Derivation of the ACF......Page 561
A.7.2 Estimation method......Page 564
B.1.1 Least squares estimators......Page 566
B.2 Linear model with basic assumptions......Page 567
B.2.1 Sampling distributions of the model parameters......Page 568
B.2.3 Analysis of variance table......Page 569
B.2.4 Coefficient of determination......Page 570
B.3 Maximum likelihood estimation method......Page 571
B.4.1 Two-stage estimation method......Page 573
B.4.3 Properties of the error termt......Page 574
B.4.4 Maximum likelihood estimation method......Page 575
B.5 AR(p) linear model......Page 576
B.5.2 Properties oft......Page 577
B.6.2 Alternative 2: The classical growth model......Page 578
B.7 Lagged-variable model......Page 579
B.8.1 The simplest lagged-variable autoregressive model......Page 580
B.8.2 General lagged-variable autoregressive model......Page 582
B.9 Special notes and comments......Page 583
C.1 General linear model with i.i.d. Gaussian disturbances......Page 584
C.1.1 The OLS estimates......Page 585
C.1.2 Maximum likelihood estimates......Page 586
C.1.4 The Wald form of the OLS F-test......Page 587
C.2 AR(1) general linear model......Page 588
C.2.2 Estimation method......Page 589
C.4 General lagged-variable autoregressive model......Page 590
C.5.1 Gaussian errors with a known variance covariance matrix......Page 591
C.5.2 Generalized least squares with a known covariance matrix......Page 592
C.5.4 The variance of the error is proportional to the square of one of the explanatory variables......Page 593
C.5.5 Generalized least squares with an unknown covariance matrix......Page 594
D.1 Multivariate general linear models......Page 596
D.2 Moments of an endogenous multivariate......Page 597
D.3 Vector autoregressive model......Page 598
D.5 Vector autoregressive moving average model......Page 599
D.6.1 The simplest multivariate model......Page 600
D.6.2 Simple model with a multidimensional exogenous variable......Page 601
D.6.4 Selected bivariate time series models......Page 602
D.6.5 Bivariate Granger causality tests......Page 603
D.7 General estimation methods......Page 604
D.7.1 The OLS estimates......Page 605
D.8 Maximum likelihood estimation for an MGLM......Page 606
D.8.2 The Wald form of the OLS F-test......Page 607
D.9.1 AR(p) MGLM with equal sets of exogenous variables......Page 608
D.9.2 AR(p) MGLM with unequal sets of exogenous variables......Page 609
D.9.3 Special notes and comments......Page 610
References......Page 612
Index......Page 616
